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Baseline_30Kphish_benignWinter_20_20_20
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0546
- Accuracy: 0.9949
- F1: 0.9438
- Precision: 0.9967
- Recall: 0.8962
- Roc Auc Score: 0.9480
- Tpr At Fpr 0.01: 0.8872
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc Score | Tpr At Fpr 0.01 |
|---|---|---|---|---|---|---|---|---|---|
| 0.0097 | 1.0 | 19688 | 0.0272 | 0.9936 | 0.9283 | 0.9869 | 0.8762 | 0.9378 | 0.7798 |
| 0.005 | 2.0 | 39376 | 0.0444 | 0.9916 | 0.9028 | 0.9985 | 0.8238 | 0.9119 | 0.8272 |
| 0.0008 | 3.0 | 59064 | 0.0382 | 0.9943 | 0.9368 | 0.9984 | 0.8824 | 0.9412 | 0.8846 |
| 0.0008 | 4.0 | 78752 | 0.0416 | 0.9952 | 0.9476 | 0.9954 | 0.9042 | 0.9520 | 0.8832 |
| 0.0 | 5.0 | 98440 | 0.0546 | 0.9949 | 0.9438 | 0.9967 | 0.8962 | 0.9480 | 0.8872 |
Framework versions
- Transformers 4.29.1
- Pytorch 1.9.0+cu111
- Datasets 2.10.1
- Tokenizers 0.13.2
